Detection and characterization of aortic pathologies

ABSTRACT

According to one or more embodiments, a method, a computer program product, and a computer system for detecting and characterizing aortic pathologies are provided. The method may include receiving, by a computer, one or more tomograph scan images corresponding to a patient&#39;s aorta. The one or more received tomograph scan images may be analyzed by the computer for one or more image features associated with one or more aortic pathologies, such as aortic dissection or an aortic aneurysm. One or more image features associated with the one or more aortic pathologies may be identified in the one or more analyzed tomograph scan images, which may allow the determination of an aortic pathology associated with the patient&#39;s aorta based on the identification of the image features. A portion of the aorta and one or more branch arteries corresponding to the determined aortic pathology may then be identified.

BACKGROUND

The present invention relates generally to field of medicine, and moreparticularly to detection and characterization of aortic pathologies,such as aortic aneurysms and aortic dissection.

The aorta is the largest artery within the human body. It originatesfrom the left ventricle and distributes oxygenated blood to all otherparts of the body. The aorta can be divided into two main regions: thethoracic aorta and the abdominal aorta. The thoracic aorta may befurther subdivided into three main sections: the ascending aorta, theaortic arch, and the descending aorta. The various regions and sectionsof the aorta may present with, among other things, one or morepathologies, such as aortic dissection or an aortic aneurysm. An aorticdissection may occur when a tear inside the intimal wall of an arteryallows blood to flow between the two layers of the vessel wall. Inaddition to the aorta, the iliac, renal and carotid arteries can also beaffected. An aortic aneurysm may occur when the aorta dilates, which maycause a thinning of the wall of the aorta. Both aortic dissections andaortic aneurysms may cause, among other things, ischemia or aorticrupture, which may pose a life-threatening medical emergency.

SUMMARY

Embodiments of the present invention disclose a method, system, andcomputer program product for detecting and characterizing aorticpathologies. According to one embodiment, a method for detecting andcharacterizing aortic pathologies is provided. The method may includereceiving, by a computer, one or more tomograph scan imagescorresponding to a patient's aorta. The one or more received tomographscan images may be analyzed by the computer for one or more imagefeatures associated with one or more aortic pathologies, such as aorticdissection or an aortic aneurysm. One or more image features associatedwith the one or more aortic pathologies may be identified in the one ormore analyzed tomograph scan images, which may allow the determinationof an aortic pathology associated with the patient's aorta based on theidentification of the image features. A portion of the aorta and one ormore branch arteries corresponding to the determined aortic pathologymay then be identified.

According to another embodiment, a computer system for detecting andcharacterizing aortic pathologies is provided. The computer system mayinclude one or more processors, one or more computer-readable memories,one or more computer-readable tangible storage devices, and programinstructions stored on at least one of the one or more storage devicesfor execution by at least one of the one or more processors via at leastone of the one or more memories, whereby the computer system is capableof performing a method. The method may include receiving, by a computer,one or more tomograph scan images corresponding to a patient's aorta.The one or more received tomograph scan images may be analyzed by thecomputer for one or more image features associated with one or moreaortic pathologies, such as aortic dissection or an aortic aneurysm. Oneor more image features associated with the one or more aorticpathologies may be identified in the one or more analyzed tomograph scanimages, which may allow the determination of an aortic pathologyassociated with the patient's aorta based on the identification of theimage features. A portion of the aorta and one or more branch arteriescorresponding to the determined aortic pathology may then be identified.

According to yet another embodiment, a computer program product fordetecting and characterizing aortic pathologies is provided. Thecomputer program product may include one or more computer-readablestorage devices and program instructions stored on at least one of theone or more tangible storage devices, the program instructionsexecutable by a processor. The program instructions are executable by aprocessor for performing a method that may accordingly includereceiving, by a computer, one or more tomograph scan imagescorresponding to a patient's aorta. The one or more received tomographscan images may be analyzed by the computer for one or more imagefeatures associated with one or more aortic pathologies, such as aorticdissection or an aortic aneurysm. One or more image features associatedwith the one or more aortic pathologies may be identified in the one ormore analyzed tomograph scan images, which may allow the determinationof an aortic pathology associated with the patient's aorta based on theidentification of the image features. A portion of the aorta and one ormore branch arteries corresponding to the determined aortic pathologymay then be identified.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 depicts an illustration of a thoracic aorta according to at leastone embodiment;

FIG. 3A is an exemplary tomograph scan image depicting a normal aorta tobe analyzed by the computer system depicted in FIG. 1, according to atleast one embodiment;

FIG. 3B is an exemplary tomograph scan image depicting an aorticdissection to be analyzed by the computer system depicted in FIG. 1,according to at least one embodiment;

FIG. 3C is an exemplary tomograph scan image depicting an aorticaneurysm to be analyzed by the computer system depicted in FIG. 1,according to at least one embodiment;

FIG. 4 is an operational flowchart illustrating the steps carried out bya program that detects and characterizes aortic pathologies, accordingto at least one embodiment;

FIG. 5 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 6 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 7 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 6, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

Embodiments of the present invention relate generally to the field ofmedicine, and more particularly to detection and characterization ofaortic pathologies, such as aortic aneurysms and aortic dissection. Thefollowing described exemplary embodiments provide a system, method andprogram product to, among other things, determine one or more aorticpathologies affecting a patient. Therefore, embodiments of the presentinvention have the capacity to improve the field of medicine by allowingenhanced diagnosis of aortic and other arterial and venous pathologiesby automatically characterizing their severity and extent. For example,a patient may present with a dilated ascending aorta having a diameterlarger than approximately six millimeters. Thus, the computer-implementmethod, computer system, and computer program product disclosed hereinmay, among other things, be used to diagnose aortic pathologies in orderto allow optimal and rapid treatment. Furthermore, while the method,system, and computer program product disclosed herein are described withrespect to aortic pathologies, the described embodiments may also beconfigured for the detection and characterization of pathologies ofother blood vessels, such as veins and other arteries.

As previously described, the aorta is the largest artery within thehuman body. The various regions and sections of the aorta may presentwith, among other things, one or more pathologies, such as aorticdissection or an aortic aneurysm, that are serious, life-threateningmedical emergencies. Often, a diagnosis of aneurysm or dissection of avessel may be made by visualization of the vessel in a contrast-enhancedcomputer tomography (CE-CT) scan of the chest and/or abdomen. Otherimaging modalities such as trans-esophageal echocardiogram and MRI canalso be used. An aortic dissection can be characterized by factors suchas type, determined by the position of a false lumen, and also extensionof the dissection to other vessels branching off the main vessel (i.e.renal, iliac and carotid arteries in an aortic dissection). Thedissection type, its extent and some other clinical indications andcontraindications are the deciding factors in the selection of thetreatment. It may, therefore, be advantageous to enhance detection andcharacterization of such aortic pathologies in order to diagnose andtreat such pathologies quickly and effectively. Accordingly, theinvention disclosed herein may improve the field of computing byproviding a system, method, and program product to detect, characterize,and suggest treatment for aortic pathologies without user intervention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

The following described exemplary embodiments provide a system, methodand program product that detects and characterizes one or more aorticpathologies in patients. According to the present embodiment, thisdetection and characterization may be provided through analysis of oneor more tomograph scan images to detect one or more image featuresassociated with the aortic pathologies. Based on the detection of theimage features, the appropriate aortic pathology may be diagnosed andtreated.

Referring now to FIG. 1, a functional block diagram illustrating anaortic pathology characterization system 100 (hereinafter “system”) forimproved detection and characterization of aortic pathologies, such asaortic aneurysms and aortic dissection, is shown. It should beappreciated that FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a software program 108 that is stored on a data storage device106 and is enabled to interface with a user and communicate with theserver computer 114. As will be discussed below with reference to FIG. 5the computer 102 may include internal components 800A and externalcomponents 900A, respectively, and the server computer 114 may includeinternal components 800B and external components 900B, respectively. Thecomputer 102 may be, for example, a mobile device, a telephone, apersonal digital assistant, a netbook, a laptop computer, a tabletcomputer, a desktop computer, or any type of computing devices capableof running a program, accessing a network, and accessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below. Theserver computer 114 may also be located in a cloud computing deploymentmodel, such as a private cloud, community cloud, public cloud, or hybridcloud. The server computer 114, which may be used for detecting andcharacterizing aortic pathologies, notifying users of the aorticpathologies, and determining optimal treatment options is enabled to runan Aortic Pathology Characterization Program 116 (hereinafter “program”)that may interact with a database 112. The Aortic PathologyCharacterization Program method is explained in more detail below withrespect to FIG. 4. In one embodiment, the computer 102 may operate as aninput device including a user interface while the program 116 may runprimarily on server computer 114. In an alternative embodiment, theprogram 116 may run primarily on one or more computers 102 while theserver computer 114 may be used for processing and storage of data usedby the program 116. It should be noted that the program 116 may be astandalone program or may be integrated into a larger aortic pathologycharacterization program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with a mailserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network, a wirelessnetwork, a public switched network and/or a satellite network.

Referring to FIG. 2, an illustration of a thoracic aorta 200 isdepicted. The thoracic aorta 200 includes an ascending aorta 202, anaortic arch 204, and a descending aorta 206. The right coronary artery208 and left coronary artery 210 branch off of the ascending aorta 202just above the aortic root. The brachiocephalic artery 212, the leftcommon carotid artery 214, and the left subclavian artery 216 eachbranch off of the aortic arch 204. The brachiocephalic artery 212 is, inturn, connected to the right subclavian artery 218 and the right commoncarotid artery 220. The abdominal aorta is inferior and distal to thedescending aorta 206 and is not depicted. One or more arteries, such asthe hepatic, mesenteric, and left and right renal arteries, may branchoff of the abdominal aorta. A tomograph scan 300 may be acquired fromany part of the aorta. It may be appreciated that while the tomographscan image 300 depicted in FIG. 2 corresponds to a cross section of thedescending aorta, the tomograph scan image 300 may also correspond torespective cross sections of the ascending aorta, aortic arch, andabdominal aorta.

With regard to FIGS. 3A-3C, one or more tomograph scan images of theaorta 200 that may be analyzed by the computer system 100 are depictedaccording to one embodiment. It may be appreciated that, by way ofexample and not of limitation, the tomograph scan images arecross-sectional images of the descending thoracic aorta. However, theone or more tomograph scan images may depict any portion of the thoracicand/or abdominal aorta, such as, for example, the aortic root, theascending aorta, or the aortic arch.

Referring now to FIG. 3A, an exemplary tomograph scan image 300Adepicting a cross-sectional view of a normal aorta, not having anydiagnosable pathologies, to be analyzed by the computer system 100depicted in FIG. 1 according to one embodiment is depicted. Tomographscan image 300A may show an aorta 200 having a vascular wall 302A, alumen 304A, and a diameter D.

Referring now to FIG. 3B, an exemplary tomograph scan image 300B of across-sectional view of an aortic dissection to be analyzed by thecomputer system 100 of FIG. 1, according to one embodiment, is depicted.Tomograph scan image 300B may show, among other things, an aorta havinga vascular wall 302B, a lumen 304B, and a false lumen 306. It may beappreciated that the tomograph scan image 300B may depict both StanfordType A and Type B aortic dissections.

Referring now to FIG. 3C is an exemplary tomograph scan image depictinga cross-sectional view of an aortic aneurysm to be analyzed by thecomputer system 100 depicted in FIG. 1, according to one embodiment.Tomograph scan image 300C may show an aorta having a vascular wall 302C,a lumen 304C, and a diameter D′. Diameter D′ may be approximately 1.5times the size of diameter D or greater. It may be appreciated that thetomograph scan image 300C may also show any type of aortic aneurysm,such as fusiform aneurysms, saccular aneurysms and pseudo-aneurysms.

Referring now to FIG. 4, an operational flowchart 400 illustrating thesteps carried out by a program that detects and characterizes aorticpathologies is depicted. FIG. 4 may be described with the aid of FIGS.1, 2, and 3A-3C. As previously described, the Aortic PathologyCharacterization Program 116 (FIG. 1) may quickly and effectivelydiagnose aortic pathologies.

At 402, one or more tomograph scan images corresponding to a patient'saorta are received by a computer. The tomograph scan images may obtainedthrough a variety of tomography methods, such as x-ray computedtomography (CT), positron emission tomography (PET), magnetic resonanceimaging (MRI), functional magnetic resonance imaging (fMRI), or anycombination of methods, such as PET-CT. According to one exemplaryembodiment, the Aortic Pathology Characterization Program 116 (FIG. 1)on the server computer 114 (FIG. 1) may receive one or more tomographscan images 300 (FIG. 2) from the computer 102 (FIG. 1) via thecommunication network 110 (FIG. 1). The received tomograph scan images300 may depict any section of the aorta, such as the ascending aorta,the aortic arch, the descending aorta, and the abdominal aorta.Furthermore, the received tomograph scan images 300 may be insubstantially any digital format, such as PNG, JPG, TIF, or anyproprietary format.

At 404, the one or more received tomograph scan images are analyzed, bya computer, for one or more images features that may be associated withan aortic pathology. For example, the one or more received tomographscan images may contain, among other things, one or more image featuresassociated with an aortic pathology, such as an aortic dissection (seeFIG. 3B) or aortic aneurysm (see FIG. 3C). The images may be analyzedusing one or more qualitative methods, such as visual landmarkdetection, false lumen detection, or ridge detection. One or more storedreference images stored within a database and accessible by the computermay be used as a basis for comparison. The one or more stored referenceimages may contain known image features and may be used to determine thepresence of the image features within the received images.Alternatively, may be one or more quantitative measurements, such asartery diameter measurements, circularity measurements, or normalizationvalues associated with the received images. In the case of aorticdissection, the Aortic Pathology Characterization Program 116 (FIG. 1)may identify the false lumen 306 (FIG. 3B) in the tomograph scan image300B (FIG. 3B) using visual landmark detection, ridge detection, orcircularity measurements. Alternatively, in the case of aortic aneurysm,the Aortic Pathology Characterization Program 116 may identify diameterD′ (FIG. 3C) in tomograph scan image 300C (FIG. 3C) as being greaterthan a predetermined threshold value. The predetermined threshold valuemay correspond generally with diameter D (FIG. 3A) associated withtomograph scan image 300A (FIG. 3A).

The aorta and possible affected regions may be localized within animage. For example the aorta and its parts (ascending, arch anddescending), the subclavian artery, the renal, iliac and carotidarteries may be localized in a medical image such as CE-CT. This taskmay be performed with an Atlas-based segmentation method in which thecurrent CT image may be registered in a deformable fashion to a group ofpreviously labeled CT images (atlases). A deformable image registrationmay be performed in order to align a group of previously labeled CTimages (atlases) to a target image for which the aorta segmentation mayneed to be produced. The previously labeled CTs may have labels for theanatomies of interest. After the registration, the labels from theatlases may be transformed to the coordinate system of the current imageto label the intended regions and organs. Using the registration-basedpropagation technique, each atlas may produce one candidate segmentationfor the target image. A label fusion step may then applied for integratethe multiple candidate segmentations into one consensus solution. It maybe appreciated that each slice in the main image may be localized to aparticular region of the aorta. The registration and label fusionprocesses may be used to segment the aorta for re-slicing, such thateach slice may be perpendicular to the main vessel (i.e. the aorta) at apoint along the vessel center line. This may be achieved by applyingprincipal component analysis at different points along the vessel centerline. In these slices, the vessel cross section may be, for example,almost circular. Thus, using this technique, the aorta may be segmentedinto three components: ascending aorta, aortic arch, and descendingaorta.

In order to detect slices indicating, for example, a dissection, thepresence of any visual landmarks that may indicate a dissection (forexample a flap or irregularity in the shape of the vessel in thecross-sectional images) may be examined for each of the slices. Thisdetection may be based on traditional image processing algorithms, suchas ridge detection and circularity measures. It may also be achieved bymachine learning algorithms where hand-engineered features may beproduced from each cross-sectional image and may be used, for example,for classifying the image into normal and dissected slices using amachine learning approach. Alternatively, deep learning algorithms maybe used to learn features directly from images. In at least oneembodiment, user feedback may be fed to the system to improve detectionaccuracy over time. Features or images of cases misidentified asdissected or healthy by the system may be fed back to the system withcorrect labels from a user to increase the accuracy of the system infuture.

At 406, the Aortic Pathology Characterization Program 116 (FIG. 1)determines whether at least one image feature associated with an aorticpathology is identified in the one or more tomograph scan images. If noimage feature is identified, Aortic Pathology Characterization Program116 determines the presence of a normal aorta, as will be discussed infurther detail with respect to 414. If, however, at least one imagefeature associated with an aortic pathology is identified in the one ormore tomograph scan images, the Aortic Pathology CharacterizationProgram 116 may determine the type and extent of the aortic pathology.It may be appreciated that to increase specificity, more than oneabnormal slice may need to be detected.

If at 406, the Aortic Pathology Characterization Program 116 (FIG. 1)determined that there is at least one image feature associated with anaortic pathology, the Aortic Pathology Characterization Program 116 maydetermine, at 408, the aortic pathology associated with the patient'saorta based on the one or more identified image features. For example,in the case of aortic dissection, the Aortic Pathology CharacterizationProgram 116 may determine the presence of an aortic dissection in thepatient's aorta based on the detection of the false lumen 306 (FIG. 3B)in tomograph scan image 300B (FIG. 3B). Alternatively, in the case ofaortic aneurysm, the Aortic Pathology Characterization Program 116 maydetermine the presence of an aortic aneurysm based on the diameter D′(FIG. 3C) of the patient's aorta being greater than the pre-determinedthreshold value that may correspond to, for example, diameter D.

At 410, at least a portion of the aorta and one or more branch arteriescorresponding to the determined aortic pathology are identified by thecomputer. The arteries affected in conjunction with the aorta may beused, among other things, to determine the type and extent of the aorticpathology. Additionally, the length of a dissection or the length anddiameter of an aneurysm may also be used to determine the extent of theaortic pathology. After detection of the dissected slices, thecoordinates of the centers of the vessel may be transformed from there-sliced images to the original labeled CT image for the slices withdissection. According to these positions the type of the aorta may bedecided. For example, if the center point of the first dissected sliceis part of the ascending aorta or the arch before the left subclavianartery, the dissection may of Type A. Alternatively, if the center pointof the first dissected slice is part of the descending aorta or archafter the left subclavian artery, the dissection may of Type B. Based onthe position of the dissected slices the system can determine whetherother arteries such as renal, iliac and carotid may also affected by thedissection. For example, in the case of aortic dissection, an aorticdissection affecting the ascending aorta and aortic arch—and, therefore,the brachiocephalic, left common carotid, and left subclavianarteries—may be characterized as a DeBakey Type I or Stanford Type Aaortic dissection. It may be, among other things, clinically importantto determine one or more affected arteries in conjunction with the aortabecause the type of aortic pathology may be the deciding factor indetermining treatment for the patient. For example, a Stanford Type Adissection may be treated with surgery, while a Stanford Type Bdissection may be treated with medication. In operation, the AorticPathology Characterization Program 116 (FIG. 1) may determine, forexample, that an aortic dissection occurs along the length of at least aportion of the ascending aorta 202 (FIG. 2), and the aortic arch 204(FIG. 2). The Aortic Pathology Characterization Program 116 may alsodetermine that the dissection affects blood flow to the brachiocephalicartery 212 (FIG. 2), the left common carotid artery 214 (FIG. 2), theleft subclavian artery 216 (FIG. 2), and the renal arteries. The AorticPathology Characterization Program 116 may therefore characterize theaortic dissection as a Stanford Type A aortic dissection that mayrequire surgical repair.

At 412, a user is notified by the computer of the determined aorticpathology, the at least a portion of the aorta corresponding to thedetermined aortic pathology and one or more treatment options associatedwith the determined aortic pathology. After detection andcharacterization of the dissection, the system may also rely on anavailable database of clinical knowledge to make a diagnosis and followup recommendations based on the findings. The clinical knowledge may beacquired via mining of medical text or entered into the system by theuser or a combination of both. As was previously discussed for 410, thearteries involved in the aortic pathology may aid in determining thetype of aortic pathology, a recommended course of treatment, and anyother next steps. This type of aortic pathology may, in turn, beimportant in determining the type of treatment a patient may receive.For example, Stanford Type A aortic dissections may be treated withsurgery, while Stanford Type B aortic dissections may be treated withmedication. According to one embodiment, the Aortic PathologyCharacterization Program 116 (FIG. 1) on the server computer 114(FIG. 1) may determine an aortic dissection affecting the ascendingaorta 202 (FIG. 2), the aortic arch 204 (FIG. 2), the brachiocephalicartery 212 (FIG. 2), the left common carotid artery 214 (FIG. 2), theleft subclavian artery 216 (FIG. 2) to be a Stanford Type A dissection.The Aortic Pathology Characterization Program 116 may then notify a userof software program 108 (FIG. 1) via communication network 110 (FIG. 1)that the received tomograph scan images 300 (FIG. 2) correspond to thetomograph scan images 300B (FIG. 3B) of a Stanford Type A aorticdissection. The Aortic Pathology Characterization Program 116 mayfurther notify the user that surgical repair is the recommendedtreatment option. The treatment option may be determined by the AorticPathology Characterization Program 116 by searching the database 112(FIG. 1) on the server computer 114 or by searching other databases 112on other server computers 114 connected via the communication network110. The database 112 may store clinical knowledge and other patientinformation that may be taken into account to propose the best treatmentoption for a patient. For example, surgery for Type A may be the bestoption if there are no contraindications identified within the storedpatient data.

If at 406, there is no image feature within the tomograph scan imagethat may be identified as being associated with an aortic pathology, thepatient's aorta may, among other things, be classified as a normal aortaby the computer. A normal aorta would not have, for example, dilation ofthe vessel associated with an aortic aneurysm nor one or more falselumens associated with aortic dissection. In operation, the AorticPathology Characterization Program 116 (FIG. 1) on the server computer114 (FIG. 1) may identify aorta 200 (FIG. 2) as being a normal aortabased on tomograph scan image 300A (FIG. 3A) lacking any image featuresassociated with one or more aortic pathologies.

At 416, a user is optionally notified by the computer that the patient'saorta is a normal aorta. In operation, the Aortic PathologyCharacterization Program 116 (FIG. 1) on the server computer 114(FIG. 1) may then notify a user of software program 108 (FIG. 1) viacommunication network 110 (FIG. 1) that the received tomograph scanimages 300 (FIG. 2) correspond to the tomograph scan images 300A (FIG.3B) of a normal aorta and that no treatment may be required at thepresent time.

At 418, the one or more tomograph scan images are stored by the computerwithin a database on the computer as one or more reference images. Theone or more reference images may be used for future comparison indetermining the presence of aortic pathologies, as was discussed abovefor 404. In operation, the Aortic Pathology Characterization Program 116(FIG. 1) may store the one or more tomograph scan images 300 (FIG. 2)within the database 112 (FIG. 1) on the server computer 114 (FIG. 1).The one or more tomograph scan images may correspond to tomograph scanimages 300A (FIG. 3A) depicting normal aortae, tomograph scan images300B (FIG. 3B) depicting one or more aortic dissections, or tomographscan images 300C (FIG. 3C) depicting one or more aortic aneurysms.

It may be appreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements. For example, as discussed above, the system disclosedherein may be used in substantially the same or similar way to detectand characterize pathologies of the heart, lungs, brain, liver, lymphnodes, and other bodily systems.

FIG. 5 is a block diagram 500 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.5 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 5. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, and one or more operating systems 828 and one or morecomputer-readable tangible storage devices 830. The one or moreoperating systems 828, the Software Program 108 (FIG. 1) and the AorticPathology Characterization Program 116 (FIG. 1) on server computer 114(FIG. 1) are stored on one or more of the respective computer-readabletangible storage devices 830 for execution by one or more of therespective processors 820 via one or more of the respective RAMs 822(which typically include cache memory). In the embodiment illustrated inFIG. 5, each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory or anyother computer-readable tangible storage device that can store acomputer program and digital information.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the SoftwareProgram 108 (FIG. 1) and the Aortic Pathology Characterization Program116 (FIG. 1) can be stored on one or more of the respective portablecomputer-readable tangible storage devices 936, read via the respectiveR/W drive or interface 832 and loaded into the respective hard drive830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The Software Program 108 (FIG. 1) and theAortic Pathology Characterization Program 116 (FIG. 1) on the servercomputer 114 (FIG. 1) can be downloaded to the computer 102 (FIG. 1) andserver computer 114 from an external computer via a network (forexample, the Internet, a local area network or other, wide area network)and respective network adapters or interfaces 836. From the networkadapters or interfaces 836, the Software Program 108 and the AorticPathology Characterization Program 116 on the server computer 114 areloaded into the respective hard drive 830. The network may comprisecopper wires, optical fibers, wireless transmission, routers, firewalls,switches, gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 6, illustrative cloud computing environment 600 isdepicted. As shown, cloud computing environment 600 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 600 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 600 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 7, a set of functional abstraction layers 700 providedby cloud computing environment 600 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Aortic Pathology Characterization 96.Aortic Pathology Characterization 96 may detect and characterize one ormore aortic pathologies, such as aortic dissection or an aorticaneurysm.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for characterizingaortic pathologies, the method comprising: identifying, by a computer,one or more image features associated with one or more aorticpathologies within one or more tomograph scan images of a patient'saorta; retrieving, by the computer from a database, one or morereference images corresponding to an aortic pathology; determining, bythe computer, an aortic pathology associated with the patient's aortafrom among the one or more aortic pathologies based on theidentification of the one or more image features and based on acomparison of the tomograph scan images to the one or more retrievedreference images; identifying, by the computer, at least a portion ofthe aorta and one or more branch arteries corresponding to thedetermined aortic pathology.
 2. The method of claim 1, furthercomprising determining one or more treatment options in response to thedetermination of the aortic pathology associated with the at least aportion of the aorta and the one or more branch arteries, wherein thetreatment option is based on the determined aortic pathology andclinical data associated with the patient.
 3. The method of claim 2,further comprising notifying a user of at least one of: the determinedaortic pathology, the at least a portion of the aorta and the one ormore branch arteries corresponding to the determined aortic pathology,and the one or more determined treatment options.
 4. The method of claim1, further comprising storing the one or more tomograph scan imageswithin the database as one or more reference images associated with oneor more aortic pathologies.
 5. The method of claim 1, wherein the aorticpathologies comprise at least one of: an aortic dissection, an abdominalaortic aneurysm, and a thoracic aortic aneurysm.
 6. The method of claim1, further comprising: analyzing, by the computer, the one or moretomograph scan images for one or more image features associated with oneor more aortic pathologies, wherein the analyzing the one or morereceived tomograph scan images for one or more image features associatedwith one or more aortic pathologies comprises at least one of: detectingone or more visual landmarks within the one or more receiving tomographscan images; detecting one or more false lumens within the one or morereceiving tomograph scan images; detecting one or more ridges within theone or more receiving tomograph scan images; measuring a diametercorresponding to the patient's aorta within the one or more receivingtomograph scan images; and measuring a circularity value associated withthe patient's aorta within the one or more receiving tomograph scanimages.
 7. The method of claim 1, further comprising: detecting a normalaorta; notifying a user of the detected normal aorta; and storing thereceived tomograph scan images corresponding to the detected normalaorta as one or more reference images associated with a normal aorta. 8.A computer program product for characterizing aortic pathologies, thecomputer program product comprising: one or more computer-readablenon-transitory storage media and program instructions stored on the oneor more computer readable non-transitory storage media, the programinstructions comprising: program instructions to identify, by acomputer, one or more image features associated with one or more aorticpathologies within one or more tomograph scan images of a patient'saorta; program instructions to retrieve, by the computer from adatabase, one or more reference images corresponding to an aorticpathology; program instructions to determine, by the computer, an aorticpathology associated with the patient's aorta from among the one or moreaortic pathologies based on the identification of the one or more imagefeatures and based on a comparison of the tomograph scan images to theone or more retrieved reference images; program instructions toidentify, by the computer, at least a portion of the aorta and one ormore branch arteries corresponding to the determined aortic pathology.9. The computer program product of claim 8, further comprising programinstructions to determine one or more treatment options in response tothe determination of the aortic pathology associated with the at least aportion of the aorta and the one or more branch arteries, wherein thetreatment option is based on the determined aortic pathology andclinical data associated with the patient.
 10. The computer programproduct of claim 9, further comprising program instructions to notify auser of at least one of: the determined aortic pathology, the at least aportion of the aorta and the one or more branch arteries correspondingto the determined aortic pathology, and the one or more determinedtreatment options.
 11. The computer program product of claim 8, furthercomprising program instructions to store the one or more tomograph scanimages within the database as one or more reference images associatedwith one or more aortic pathologies.
 12. The computer program product ofclaim 8, wherein the aortic pathologies comprise at least one of: anaortic dissection, an abdominal aortic aneurysm, and a thoracic aorticaneurysm.
 13. The computer program product of claim 8, furthercomprising program instructions to analyze, by the computer, the one ormore tomograph scan images for one or more image features associatedwith one or more aortic pathologies, wherein the analyzing the one ormore received tomograph scan images for one or more image featuresassociated with one or more aortic pathologies comprises at least oneof: program instructions to detect one or more visual landmarks withinthe one or more receiving tomograph scan images; program instructions todetect one or more false lumens within the one or more receivingtomograph scan images; program instructions to detect one or more ridgeswithin the one or more receiving tomograph scan images; programinstructions to measure a diameter corresponding to the patient's aortawithin the one or more receiving tomograph scan images; and programinstructions to measure a circularity value associated with thepatient's aorta within the one or more receiving tomograph scan images.14. The computer program product of claim 8, further comprising: programinstructions to detect a normal aorta; program instructions to notify auser of the detected normal aorta; and program instructions to store thereceived tomograph scan images corresponding to the detected normalaorta as one or more reference images associated with a normal aorta.15. A computer system for characterizing aortic pathologies, thecomputer system comprising: one or more computer processors, one or morecomputer-readable non-transitory storage media, and program instructionsstored on the one or more computer-readable non-transitory storage mediafor execution by at least one of the one or more computer processors,the program instructions comprising: program instructions to identify,by a computer, one or more image features associated with one or moreaortic pathologies within one or more tomograph scan images of apatient's aorta; program instructions to retrieve, by the computer froma database, one or more reference images corresponding to an aorticpathology; program instructions to determine, by the computer, an aorticpathology associated with the patient's aorta from among the one or moreaortic pathologies based on the identification of the one or more imagefeatures and based on a comparison of the tomograph scan images to theone or more retrieved reference images; program instructions toidentify, by the computer, at least a portion of the aorta and one ormore branch arteries corresponding to the determined aortic pathology,wherein the aortic pathologies comprise at least one of an aorticdissection, an abdominal aortic aneurysm, and a thoracic aorticaneurysm.
 16. The computer system of claim 15, further comprisingprogram instructions to determine one or more treatment options inresponse to the determination of the aortic pathology associated withthe at least a portion of the aorta and the one or more branch arteries,wherein the treatment option is based on the determined aortic pathologyand clinical data associated with the patient.
 17. The computer systemof claim 16, further comprising program instructions to notify a user ofat least one of: the determined aortic pathology, the at least a portionof the aorta and the one or more branch arteries corresponding to thedetermined aortic pathology, and the one or more determined treatmentoptions.
 18. The computer system of claim 15, further comprising programinstructions to store the one or more tomograph scan images within thedatabase as one or more reference images associated with one or moreaortic pathologies.
 19. The computer system of claim 15, furthercomprising program instructions to analyze, by the computer, the one ormore received tomograph scan images for one or more image featuresassociated with one or more aortic pathologies, wherein the analyzingthe one or more received tomograph scan images for one or more imagefeatures associated with one or more aortic pathologies comprises atleast one of: program instructions to detect one or more visuallandmarks within the one or more receiving tomograph scan images;program instructions to detect one or more false lumens within the oneor more receiving tomograph scan images; program instructions to detectone or more ridges within the one or more receiving tomograph scanimages; program instructions to measure a diameter corresponding to thepatient's aorta within the one or more receiving tomograph scan images;and program instructions to measure a circularity value associated withthe patient's aorta within the one or more receiving tomograph scanimages.
 20. The computer system of claim 15, further comprising: programinstructions to detect a normal aorta; program instructions to notify auser of the detected normal aorta; and program instructions to store thereceived tomograph scan images corresponding to the detected normalaorta as one or more reference images associated with a normal aorta.